A PDF-Based Microphysics Parameterization for Shallow Cumulus Clouds

Abstract Unbiased calculations of microphysical process rates such as autoconversion and accretion in mesoscale numerical weather prediction models require that subgrid-scale (SGS) variability over the model grid volume be taken into account. This variability can be expressed as probability distribution functions (PDFs) of microphysical variables. Using dynamically balanced large-eddy simulation (LES) model results from a case of marine trade cumulus, the authors develop PDFs of the cloud water, droplet concentration, and rainwater variables (qc, Nc, and qr). Both 1D and 2D joint PDFs (JPDFs) are presented. The authors demonstrate that accounting for the JPDFs results in more accurate process rates for a regional-model grid size. Bias in autoconversion and accretion rates are presented, assuming different formulations of the JPDFs. Approximating the 2D PDF using a product of individual 1D PDFs overestimates the autoconversion rates by an order of magnitude, whereas neglecting the SGS variability altogether results in a drastic underestimate of the grid-mean autoconversion rate. PDF assumptions have a much smaller impact on accretion, largely because of the near-linear dependence of the variables in the accretion rate formula and the relatively weak correlation between qc and qr over the small LES grid volumes. The latter is attributed to the spatial decorrelation in the vertical between the two fields. Although the full PDFs are both height and time dependent, results suggest that fixed-in-time and fixed-in-height PDFs give an acceptable level of accuracy, especially for the crucial autoconversion calculation.

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A PDF-Based Formulation of Microphysical Variability in Cumulus Congestus Clouds*

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-15-0129.1 ◽

2015 ◽

Vol 73 (1) ◽

pp. 167-184 ◽

Cited By ~ 3

Author(s):

Yefim L. Kogan ◽

David B. Mechem

Keyword(s):

Mesoscale Model ◽

Distribution Functions ◽

Cumulus Clouds ◽

Atmospheric Sciences ◽

Microphysical Process ◽

Eddy Simulation ◽

Shallow Cumulus ◽

Toga Coare ◽

Accretion Rates ◽

Cumulus Congestus

Abstract Calculating unbiased microphysical process rates over mesoscale model grid volumes necessitates knowledge of the subgrid-scale (SGS) distribution of variables, typically represented as probability distribution functions (PDFs) of the prognostic variables. In the 2014 Journal of the Atmospheric Sciences paper by Kogan and Mechem, they employed large-eddy simulation of Rain in Cumulus over the Ocean (RICO) trade cumulus to develop PDFs and joint PDFs of cloud water, rainwater, and droplet concentration. In this paper, the approach of Kogan and Mechem is extended to deeper, precipitating cumulus congestus clouds as represented by a simulation based on conditions from the TOGA COARE field campaign. The fidelity of various PDF approximations was assessed by evaluating errors in estimating autoconversion and accretion rates. The dependence of the PDF shape on grid-mean variables is much stronger in congestus clouds than in shallow cumulus. The PDFs obtained from the TOGA COARE simulations for the calculation of accretion rates may be applied to both shallow and congestus cumulus clouds. However, applying the TOGA COARE PDFs to calculate autoconversion rates introduces unacceptably large errors in shallow cumulus clouds, thus precluding the use of a “universal” PDF formulation for both cloud types.

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LES study of microphysical variability bias in shallow cumulus

Advances in Science and Research ◽

10.5194/asr-14-103-2017 ◽

2017 ◽

Vol 14 ◽

pp. 103-107 ◽

Cited By ~ 1

Author(s):

Yefim Kogan

Keyword(s):

Weather Prediction ◽

Joint Probability ◽

Distribution Functions ◽

Rain Water ◽

Joint Probability Distribution ◽

Negative Bias ◽

Eddy Simulation ◽

Long Run ◽

Accretion Rates ◽

Nwp Model

Abstract. Subgrid-scale (SGS) variability of cloud microphysical variables over the mesoscale numerical weather prediction (NWP) model has been evaluated by means of joint probability distribution functions (JPDFs). The latter were obtained using dynamically balanced Large Eddy Simulation (LES) model dataset from a case of marine trade cumulus initialized with soundings from Rain in Cumulus Over the Ocean (RICO) field project. Bias in autoconversion and accretion rates from different formulations of the JPDFs was analyzed. Approximating the 2-D PDF using a generic (fixed-in-time), but variable-in-height JPDFs give an acceptable level of accuracy, whereas neglecting the SGS variability altogether results in a substantial underestimate of the grid-mean total conversion rate and producing negative bias in rain water. Nevertheless the total effect on rain formation may be uncertain in the long run due to the fact that the negative bias in rain water may be counterbalanced by the positive bias in cloud water. Consequently, the overall effect of SGS neglect needs to be investigated in direct simulations with a NWP model.

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Demistify: a large-eddy simulation (LES) and single-column model (SCM) intercomparison of radiation fog

Atmospheric Chemistry and Physics ◽

10.5194/acp-22-319-2022 ◽

2022 ◽

Vol 22 (1) ◽

pp. 319-333

Author(s):

Ian Boutle ◽

Wayne Angevine ◽

Jian-Wen Bao ◽

Thierry Bergot ◽

Ritthik Bhattacharya ◽

...

Keyword(s):

Large Eddy Simulation ◽

Weather Prediction ◽

Cloud Droplet ◽

Droplet Size Distribution ◽

Radiation Fog ◽

Eddy Simulation ◽

Cloud Droplet Size Distribution ◽

Large Eddy ◽

Single Column ◽

Les Model

Abstract. An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the Local and Non-local Fog Experiment (LANFEX) field campaign. Seven of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst three are research-grade SCMs designed for fog simulation, and the LESs are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number concentration (CDNC) conditions. The main SCM bias appears to be toward the overdevelopment of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parameterisation, as it is to the underlying aerosol or CDNC.

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Demistify: an LES and SCM intercomparison of radiation fog

10.5194/acp-2021-832 ◽

2021 ◽

Author(s):

Ian Boutle ◽

Wayne Angevine ◽

Jian-Wen Bao ◽

Thierry Bergot ◽

Ritthik Bhattacharya ◽

...

Keyword(s):

Weather Prediction ◽

Cloud Droplet ◽

Droplet Size Distribution ◽

Radiation Fog ◽

Eddy Simulation ◽

Cloud Droplet Size Distribution ◽

Single Column ◽

Research Grade ◽

Model Variability ◽

Les Model

Abstract. An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the LANFEX field campaign. 7 of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst 3 are research-grade SCMs designed for fog simulation, and the LES are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality, and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number (CDNC) conditions. The main SCM bias appears to be toward over-development of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP-SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parametrization, as it is to the underlying aerosol or CDNC.

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Supersaturation calculation in large eddy simulation models for prediction of the droplet number concentration

Geoscientific Model Development ◽

10.5194/gmd-5-761-2012 ◽

2012 ◽

Vol 5 (3) ◽

pp. 761-772 ◽

Cited By ~ 6

Author(s):

O. Thouron ◽

J.-L. Brenguier ◽

F. Burnet

Keyword(s):

Cloud Condensation Nuclei ◽

Simulation Models ◽

Number Concentration ◽

Eddy Simulation ◽

Droplet Concentration ◽

Large Eddy ◽

Condensed Water ◽

Les Model ◽

Adjustment Scheme ◽

Cloud Core

Abstract. A new parameterization scheme is described for calculation of supersaturation in LES models that specifically aims at the simulation of cloud condensation nuclei (CCN) activation and prediction of the droplet number concentration. The scheme is tested against current parameterizations in the framework of the Meso-NH LES model. It is shown that the saturation adjustment scheme, based on parameterizations of CCN activation in a convective updraft, overestimates the droplet concentration in the cloud core, while it cannot simulate cloud top supersaturation production due to mixing between cloudy and clear air. A supersaturation diagnostic scheme mitigates these artefacts by accounting for the presence of already condensed water in the cloud core, but it is too sensitive to supersaturation fluctuations at cloud top and produces spurious CCN activation during cloud top mixing. The proposed pseudo-prognostic scheme shows performance similar to the diagnostic one in the cloud core but significantly mitigates CCN activation at cloud top.

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Seamless Stratocumulus Simulation across the Turbulent Gray Zone

Monthly Weather Review ◽

10.1175/mwr-d-13-00229.1 ◽

2014 ◽

Vol 142 (4) ◽

pp. 1655-1668 ◽

Cited By ~ 98

Author(s):

I. A. Boutle ◽

J. E. J. Eyre ◽

A. P. Lock

Keyword(s):

Boundary Layer ◽

Prediction Models ◽

Weather Prediction ◽

Eddy Simulation ◽

Large Eddy ◽

Aircraft Observations ◽

Microphysical Parameterization ◽

Grid Length ◽

Numerical Weather Prediction Models

Abstract A pragmatic approach for representing partially resolved turbulence in numerical weather prediction models is introduced and tested. The method blends a conventional boundary layer parameterization, suitable for large grid lengths, with a subgrid turbulence scheme suitable for large-eddy simulation. The key parameter for blending the schemes is the ratio of grid length to boundary layer depth. The new parameterization is combined with a scale-aware microphysical parameterization and tested on a case study forecast of stratocumulus evolution. Simulations at a range of model grid lengths between 1 km and 100 m are compared to aircraft observations. The improved microphysical representation removes the correlation between precipitation rate and model grid length, while the new turbulence parameterization improves the transition from unresolved to resolved turbulence as grid length is reduced.

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On the utility of individual tendency output: Revealing interactions between parameterised processes during a marine cold air outbreak

Weather and Forecasting ◽

10.1175/waf-d-21-0014.1 ◽

2021 ◽

Author(s):

Marvin Kähnert ◽

Harald Sodemann ◽

Wim C. de Rooy ◽

Teresa M. Valkonen

Keyword(s):

Prediction Models ◽

Weather Prediction ◽

Specific Humidity ◽

Shallow Convection ◽

Cold Air ◽

Cold Air Outbreak ◽

Order Of Magnitude ◽

Underlying Causes ◽

The Individual

AbstractForecasts of marine cold air outbreaks critically rely on the interplay of multiple parameterisation schemes to represent sub-grid scale processes, including shallow convection, turbulence, and microphysics. Even though such an interplay has been recognised to contribute to forecast uncertainty, a quantification of this interplay is still missing. Here, we investigate the tendencies of temperature and specific humidity contributed by individual parameterisation schemes in the operational weather prediction model AROME-Arctic. From a case study of an extensive marine cold air outbreak over the Nordic Seas, we find that the type of planetary boundary layer assigned by the model algorithm modulates the contribution of individual schemes and affects the interactions between different schemes. In addition, we demonstrate the sensitivity of these interactions to an increase or decrease in the strength of the parameterised shallow convection. The individual tendencies from several parameterisations can thereby compensate each other, sometimes resulting in a small residual. In some instances this residual remains nearly unchanged between the sensitivity experiments, even though some individual tendencies differ by up to an order of magnitude. Using the individual tendency output, we can characterise the subgrid-scale as well as grid-scale responses of the model and trace them back to their underlying causes. We thereby highlight the utility of individual tendency output for understanding process-related differences between model runs with varying physical configurations and for the continued development of numerical weather prediction models.

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Large eddy simulation of urban features for Copenhagen metropolitan area

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-5-11183-2005 ◽

2005 ◽

Vol 5 (6) ◽

pp. 11183-11213 ◽

Cited By ~ 5

Author(s):

A. Mahura ◽

S. Leroyer ◽

P. Mestayer ◽

I. Calmet ◽

S. Dupont ◽

...

Keyword(s):

Metropolitan Area ◽

Prediction Models ◽

Weather Prediction ◽

Soil Layer ◽

Urban Canopy ◽

Heat Fluxes ◽

Eddy Simulation ◽

Temporal And Spatial Variability ◽

Large Eddy ◽

Urban Canopy Parameterizations

Abstract. The large eddy simulations employing the SUBMESO model with the urban soil layer model SM2-U were performed for the model domain covering the Danish Island of Sealand and including the Copenhagen metropolitan area. Monthly and diurnal cycle variability were studied for the net radiation, sensible and storage heat fluxes, surface's temperatures, and others. These were evaluated for selected urban vs. non urban related types of covers/surfaces and urban districts such as city center, high buildings, industrial, and residential. Results showed strong effects of urban features on temporal and spatial variability. They are useful and applicable for verification of numerical weather prediction models and development of urban canopy parameterizations.

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The impact of Secondary Ice Processes on a stratocumulus-to-cumulus transition during a Cold-Air Outbreak

10.5194/egusphere-egu21-3369 ◽

2021 ◽

Author(s):

Michail Karalis ◽

Georgia Sotiropoulou ◽

Steven J. Abel ◽

Elissavet Bossioli ◽

Paraskevi Georgakaki ◽

...

Keyword(s):

Prediction Models ◽

Weather Prediction ◽

Cumulus Clouds ◽

Cold Air ◽

Atmospheric Sciences ◽

Boundary Layer Clouds ◽

Cold Air Outbreak ◽

Stratocumulus Clouds ◽

Break Up ◽

The Impact

The representation of boundary layer clouds during marine Cold-Air Outbreaks (CAO) remains a great challenge for weather prediction models. Recent studies have shown that the representation of the transition from stratocumulus clouds to convective cumulus open cells largely depends on microphysical and precipitation processes, while Abel et al. (2017) further suggested that Secondary Ice Processes (SIP) may play a crucial role in the evolution of the cloud fields. In this study we use the Weather Research Forecasting model to investigate the impact of the most well-known SIP mechanisms (rime-splintering or Hallet-Mossop, mechanical break-up upon collisions between ice particles and drop-shattering) on a CAO case observed north of UK in 2013. While Hallet-Mossop is the only SIP process extensively implemented in atmospheric models, our results indicate that collisional break-up is also important in these conditions.&#160;Abel, S. J., Boutle, I. A., Waite, K., Fox, S., Brown, P. R. A., Cotton, R., Lloyd, G., Choularton, T. W., & Bower, K. N. (2017). The Role of Precipitation in Controlling the Transition from Stratocumulus to Cumulus Clouds in a Northern Hemisphere Cold-Air Outbreak,&#160;Journal of the Atmospheric Sciences,&#160;74(7), 2293-2314. Retrieved Jan 9, 2021, from&#160;https://journals.ametsoc.org/view/journals/atsc/74/7/jas-d-16-0362.1.xml

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An efficient method to account for microphysical inhomogeneity in mesoscale models by using one-dimensional variability factor.

10.5194/egusphere-egu2020-5951 ◽

2020 ◽

Author(s):

Yefim Kogan

Keyword(s):

Joint Probability ◽

Distribution Functions ◽

Joint Probability Distribution ◽

Ambient Conditions ◽

Cloud Type ◽

Mesoscale Models ◽

One Dimensional ◽

Microphysical Process ◽

Surface Precipitation ◽

Eddy Simulation

Neglecting subgrid-scale (SGS) variability can lead to substantial bias in calculations of microphysical process rates. The solution to the SGS variability bias problem lies in representing the variability using two-dimensional joint probability distribution functions (JPDFs) for the pairs of different microphysical variables. The JPDFs have also been shown to vary in the vertical: as a result their implementation in mesoscale models presents a challenging task.We developed a more efficient formulation of cloud inhomogeneity by using a concept of &#8220;generic&#8221; JPDF. Using Large Eddy Simulation (LES) studies of shallow cumulus and cumulus congestus clouds we showed that JPDFs calculated based on datasets representing &#8220;typical&#8221; cloud types (&#8220;generic&#8221; JPDFs) provide good approximation of microphysical process rates. The generic JPDF, therefore, represent the cloud type in general, i.e. they do not depend on changing ambient conditions. The advantage of generic JPDFs is that they can be a-priory integrated and yield a one-dimensional variability factor (V-factor) specific for each cloud type. A quite accurate approximation of V-factors by an analytical function in the form of a 3rd order polynomial was obtained and can be easily implemented in mesoscale models.How big is the effect of cloud inhomogeneity on precipitation? To answer this question we evaluated the effect of accounting for cloud inhomogeneity on precipitation in sensitivity simulations. In the shallow Cu case over the 24 hr simulation the surface precipitation increased by about 40% when inhomogeneity was accounted. In the congestus Cu case the increase in precipitation was even more significant: by more than 75% over only 8 hours since rain first appeared at the surface. The sensitivity experiments also revealed that most of the increase resulted from the augmented autoconversion process. The effect of modified by the V-factor accretion rates was much less significant, primarily, because of the nearly linear dependence of accretion on its parameters.&#160; This shows importance of the most accurate formulation of the autoconversion process.&#160;&#160;

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